{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据分析\n",
    "\n",
    "## 实验数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "悬尾实验的实验数据如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (30, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>小鼠编号</th><th>不动时间</th><th>所属组别</th><th>记录人</th><th>备注</th></tr><tr><td>i64</td><td>f64</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>1</td><td>11.0</td><td>&quot;空白对照组&quot;</td><td>&quot;王孟德&quot;</td><td>null</td></tr><tr><td>2</td><td>0.0</td><td>&quot;空白对照组&quot;</td><td>&quot;于小淞&quot;</td><td>null</td></tr><tr><td>3</td><td>0.0</td><td>&quot;空白对照组&quot;</td><td>&quot;黄子翾&quot;</td><td>null</td></tr><tr><td>4</td><td>21.0</td><td>&quot;空白对照组&quot;</td><td>&quot;陈文翰&quot;</td><td>null</td></tr><tr><td>5</td><td>49.0</td><td>&quot;空白对照组&quot;</td><td>&quot;陈文翰&quot;</td><td>null</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>26</td><td>206.0</td><td>&quot;利血平+小九度&quot;</td><td>&quot;于小淞&quot;</td><td>null</td></tr><tr><td>27</td><td>198.0</td><td>&quot;利血平+小九度&quot;</td><td>&quot;王孟德&quot;</td><td>null</td></tr><tr><td>28</td><td>228.0</td><td>&quot;利血平+小九度&quot;</td><td>&quot;黄子翾&quot;</td><td>null</td></tr><tr><td>29</td><td>173.0</td><td>&quot;利血平+小九度&quot;</td><td>&quot;黄子翾&quot;</td><td>null</td></tr><tr><td>30</td><td>156.0</td><td>&quot;利血平+小九度&quot;</td><td>&quot;黄子翾&quot;</td><td>null</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (30, 5)\n",
       "┌──────────┬──────────┬───────────────┬────────┬──────┐\n",
       "│ 小鼠编号 ┆ 不动时间 ┆ 所属组别      ┆ 记录人 ┆ 备注 │\n",
       "│ ---      ┆ ---      ┆ ---           ┆ ---    ┆ ---  │\n",
       "│ i64      ┆ f64      ┆ str           ┆ str    ┆ str  │\n",
       "╞══════════╪══════════╪═══════════════╪════════╪══════╡\n",
       "│ 1        ┆ 11.0     ┆ 空白对照组    ┆ 王孟德 ┆ null │\n",
       "│ 2        ┆ 0.0      ┆ 空白对照组    ┆ 于小淞 ┆ null │\n",
       "│ 3        ┆ 0.0      ┆ 空白对照组    ┆ 黄子翾 ┆ null │\n",
       "│ 4        ┆ 21.0     ┆ 空白对照组    ┆ 陈文翰 ┆ null │\n",
       "│ 5        ┆ 49.0     ┆ 空白对照组    ┆ 陈文翰 ┆ null │\n",
       "│ …        ┆ …        ┆ …             ┆ …      ┆ …    │\n",
       "│ 26       ┆ 206.0    ┆ 利血平+小九度 ┆ 于小淞 ┆ null │\n",
       "│ 27       ┆ 198.0    ┆ 利血平+小九度 ┆ 王孟德 ┆ null │\n",
       "│ 28       ┆ 228.0    ┆ 利血平+小九度 ┆ 黄子翾 ┆ null │\n",
       "│ 29       ┆ 173.0    ┆ 利血平+小九度 ┆ 黄子翾 ┆ null │\n",
       "│ 30       ┆ 156.0    ┆ 利血平+小九度 ┆ 黄子翾 ┆ null │\n",
       "└──────────┴──────────┴───────────────┴────────┴──────┘"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import polars as pl\n",
    "import pandas as pd\n",
    "import os, sys\n",
    "from pprint import pprint\n",
    "\n",
    "lf1_raw = pl.read_excel(\"data.xlsx\", sheet_name=\"悬尾实验\").lazy()\n",
    "\n",
    "lf1_raw.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "强迫游泳实验的实验数据如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (30, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>小鼠编号</th><th>不动时间</th><th>所属组别</th><th>记录人</th><th>备注</th></tr><tr><td>i64</td><td>i64</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>1</td><td>78</td><td>&quot;空白对照组&quot;</td><td>&quot;黄子翾&quot;</td><td>null</td></tr><tr><td>2</td><td>98</td><td>&quot;空白对照组&quot;</td><td>&quot;黄雨欣&quot;</td><td>null</td></tr><tr><td>3</td><td>104</td><td>&quot;空白对照组&quot;</td><td>&quot;黄雨欣&quot;</td><td>null</td></tr><tr><td>4</td><td>31</td><td>&quot;空白对照组&quot;</td><td>&quot;黄雨欣&quot;</td><td>null</td></tr><tr><td>5</td><td>83</td><td>&quot;空白对照组&quot;</td><td>&quot;黄雨欣&quot;</td><td>null</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>26</td><td>166</td><td>&quot;利血平+小九度&quot;</td><td>&quot;王孟德&quot;</td><td>null</td></tr><tr><td>27</td><td>240</td><td>&quot;利血平+小九度&quot;</td><td>&quot;placeholder&quot;</td><td>&quot;溺水&quot;</td></tr><tr><td>28</td><td>167</td><td>&quot;利血平+小九度&quot;</td><td>&quot;王孟德&quot;</td><td>null</td></tr><tr><td>29</td><td>149</td><td>&quot;利血平+小九度&quot;</td><td>&quot;王孟德&quot;</td><td>null</td></tr><tr><td>30</td><td>169</td><td>&quot;利血平+小九度&quot;</td><td>&quot;于小淞&quot;</td><td>&quot;中途退出&quot;</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (30, 5)\n",
       "┌──────────┬──────────┬───────────────┬─────────────┬──────────┐\n",
       "│ 小鼠编号 ┆ 不动时间 ┆ 所属组别      ┆ 记录人      ┆ 备注     │\n",
       "│ ---      ┆ ---      ┆ ---           ┆ ---         ┆ ---      │\n",
       "│ i64      ┆ i64      ┆ str           ┆ str         ┆ str      │\n",
       "╞══════════╪══════════╪═══════════════╪═════════════╪══════════╡\n",
       "│ 1        ┆ 78       ┆ 空白对照组    ┆ 黄子翾      ┆ null     │\n",
       "│ 2        ┆ 98       ┆ 空白对照组    ┆ 黄雨欣      ┆ null     │\n",
       "│ 3        ┆ 104      ┆ 空白对照组    ┆ 黄雨欣      ┆ null     │\n",
       "│ 4        ┆ 31       ┆ 空白对照组    ┆ 黄雨欣      ┆ null     │\n",
       "│ 5        ┆ 83       ┆ 空白对照组    ┆ 黄雨欣      ┆ null     │\n",
       "│ …        ┆ …        ┆ …             ┆ …           ┆ …        │\n",
       "│ 26       ┆ 166      ┆ 利血平+小九度 ┆ 王孟德      ┆ null     │\n",
       "│ 27       ┆ 240      ┆ 利血平+小九度 ┆ placeholder ┆ 溺水     │\n",
       "│ 28       ┆ 167      ┆ 利血平+小九度 ┆ 王孟德      ┆ null     │\n",
       "│ 29       ┆ 149      ┆ 利血平+小九度 ┆ 王孟德      ┆ null     │\n",
       "│ 30       ┆ 169      ┆ 利血平+小九度 ┆ 于小淞      ┆ 中途退出 │\n",
       "└──────────┴──────────┴───────────────┴─────────────┴──────────┘"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lf2_raw = pl.read_excel(\"data.xlsx\", sheet_name=\"强迫游泳实验\").lazy()\n",
    "\n",
    "lf2_raw.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据概览："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x2400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fm\n",
    "import matplotlib as mpl\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "try:\n",
    "\n",
    "    fm.fontManager.addfont(\"/home/jimmy/.fonts/Dengl.ttf\")\n",
    "    #fm.fontManager.addfont(\"/home/jimmy/.fonts/arial.ttf\")\n",
    "    #fm.fontManager.addfont(\"/home/jimmy/.local/share/fonts/Monospace/Monospace.ttf\")\n",
    "\n",
    "\n",
    "except FileNotFoundError as err:\n",
    "    print(f\"Failed to find file: {err}\")\n",
    "\n",
    "rc = {\n",
    "        'font.family': 'sans-serif',  # 设置字体系列为无衬线字体\n",
    "        'font.sans-serif': [\"dengxian\"],  # 列出你想要尝试的字体，按优先级排序\n",
    "        'axes.unicode_minus': True  # 解决负号显示问题\n",
    "    }\n",
    "['Noto Sans CJK SC', 'SimHei', 'Heiti SC', 'Calibri', 'Arial', \"等线\"]\n",
    "sns.set_theme(style=\"whitegrid\", rc=rc)\n",
    "\n",
    "fig, axs = plt.subplots(2,1, figsize=(8,8), sharey=True, dpi=300)\n",
    "\n",
    "sns.barplot(data=lf1_raw.collect(), x=\"小鼠编号\", y=\"不动时间\", ax=axs[0], hue=\"所属组别\")\n",
    "sns.barplot(data=lf2_raw.collect(), x=\"小鼠编号\", y=\"不动时间\", ax=axs[1], hue=\"所属组别\")\n",
    "\n",
    "axs[0].set_ylabel(\"不动时间 / s\")\n",
    "\n",
    "fig.suptitle(\"实验数据\")\n",
    "\n",
    "axs[0].set_title(\"悬尾实验\")\n",
    "axs[1].set_title(\"强迫游泳实验\")\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据分析\n",
    "\n",
    "### 质量控制\n",
    "\n",
    "由图可知，样本的数据差异很大，需要对其进行质量控制。假定总体服从正态分布，剔除偏离平均值 2 倍标准差的数据，以去除异常值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "\n",
    "def quality_control(lf: pl.LazyFrame, deviation: float = 3) -> pl.LazyFrame:\n",
    "    \"\"\"\n",
    "    Purge data deviated from the mean by standard deviation.\n",
    "    \"\"\"\n",
    "    # 获取所有组别\n",
    "    groups = lf.select(\"所属组别\").unique().collect().to_series().to_list()\n",
    "\n",
    "    stats_list: list[dict[str, np.float64 | str]] = []\n",
    "\n",
    "    # 拟合正态分布，获取均值和标准差\n",
    "    for group in groups:\n",
    "        data = lf.filter(pl.col(\"所属组别\") == group).select(\"不动时间\").collect().to_series().drop_nulls()\n",
    "        res = norm.fit(data)\n",
    "        stats_list.append({\"所属组别\": group, \"mean\": res[0], \"sd\": res[1]})\n",
    "\n",
    "    stats_lf = pl.LazyFrame(stats_list)\n",
    "\n",
    "    # 将 \"不动时间\" 的统计量添加到 LazyFrame 中\n",
    "    lf = lf.join(stats_lf, on=\"所属组别\")\n",
    "\n",
    "    # 使用辅助函数来过滤数据\n",
    "    filtered_lf = lf.filter(\n",
    "        (pl.col(\"mean\") - pl.col(\"sd\") * deviation < pl.col(\"不动时间\")) & (pl.col(\"不动时间\") < pl.col(\"mean\") + pl.col(\"sd\") * deviation)\n",
    "    )\n",
    "    \n",
    "    return filtered_lf\n",
    "\n",
    "lf1 = quality_control(lf1_raw,2)\n",
    "lf2 = quality_control(lf2_raw,2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x2400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, axs = plt.subplots(2,1, figsize=(8,8), sharey=True, dpi=300)\n",
    "\n",
    "sns.barplot(data=lf1.collect(), x=\"小鼠编号\", y=\"不动时间\", ax=axs[0], hue=\"所属组别\")\n",
    "sns.barplot(data=lf2.collect(), x=\"小鼠编号\", y=\"不动时间\", ax=axs[1], hue=\"所属组别\")\n",
    "\n",
    "axs[0].set_ylabel(\"不动时间 / s\")\n",
    "\n",
    "fig.suptitle(\"实验数据\")\n",
    "\n",
    "axs[0].set_title(\"悬尾实验\")\n",
    "axs[1].set_title(\"强迫游泳实验\")\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### 造模分析\n",
    "\n",
    "对利血平模型对照组与空白对照组的数据进行相关性分析，进而判断造模是否有效。\n",
    "\n",
    "结果变量为数值变量，影响变量为二项分类变量，应采用 t-test 分析二者的相关性。\n",
    "\n",
    "#### 悬尾实验\n",
    "\n",
    "空白对照组和模型对照组实验数据如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (10, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>小鼠编号</th><th>不动时间</th><th>所属组别</th><th>记录人</th><th>备注</th><th>mean</th><th>sd</th></tr><tr><td>i64</td><td>f64</td><td>str</td><td>str</td><td>str</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>1</td><td>11.0</td><td>&quot;空白对照组&quot;</td><td>&quot;王孟德&quot;</td><td>null</td><td>13.5</td><td>17.651723</td></tr><tr><td>2</td><td>0.0</td><td>&quot;空白对照组&quot;</td><td>&quot;于小淞&quot;</td><td>null</td><td>13.5</td><td>17.651723</td></tr><tr><td>3</td><td>0.0</td><td>&quot;空白对照组&quot;</td><td>&quot;黄子翾&quot;</td><td>null</td><td>13.5</td><td>17.651723</td></tr><tr><td>4</td><td>21.0</td><td>&quot;空白对照组&quot;</td><td>&quot;陈文翰&quot;</td><td>null</td><td>13.5</td><td>17.651723</td></tr><tr><td>6</td><td>0.0</td><td>&quot;空白对照组&quot;</td><td>&quot;陈文翰&quot;</td><td>null</td><td>13.5</td><td>17.651723</td></tr><tr><td>8</td><td>204.5</td><td>&quot;利血平&quot;</td><td>&quot;于小淞&quot;</td><td>null</td><td>146.583333</td><td>65.183342</td></tr><tr><td>9</td><td>178.0</td><td>&quot;利血平&quot;</td><td>&quot;王孟德&quot;</td><td>null</td><td>146.583333</td><td>65.183342</td></tr><tr><td>10</td><td>163.0</td><td>&quot;利血平&quot;</td><td>&quot;王孟德&quot;</td><td>null</td><td>146.583333</td><td>65.183342</td></tr><tr><td>11</td><td>180.0</td><td>&quot;利血平&quot;</td><td>&quot;王孟德&quot;</td><td>null</td><td>146.583333</td><td>65.183342</td></tr><tr><td>12</td><td>148.0</td><td>&quot;利血平&quot;</td><td>&quot;王孟德&quot;</td><td>null</td><td>146.583333</td><td>65.183342</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (10, 7)\n",
       "┌──────────┬──────────┬────────────┬────────┬──────┬────────────┬───────────┐\n",
       "│ 小鼠编号 ┆ 不动时间 ┆ 所属组别   ┆ 记录人 ┆ 备注 ┆ mean       ┆ sd        │\n",
       "│ ---      ┆ ---      ┆ ---        ┆ ---    ┆ ---  ┆ ---        ┆ ---       │\n",
       "│ i64      ┆ f64      ┆ str        ┆ str    ┆ str  ┆ f64        ┆ f64       │\n",
       "╞══════════╪══════════╪════════════╪════════╪══════╪════════════╪═══════════╡\n",
       "│ 1        ┆ 11.0     ┆ 空白对照组 ┆ 王孟德 ┆ null ┆ 13.5       ┆ 17.651723 │\n",
       "│ 2        ┆ 0.0      ┆ 空白对照组 ┆ 于小淞 ┆ null ┆ 13.5       ┆ 17.651723 │\n",
       "│ 3        ┆ 0.0      ┆ 空白对照组 ┆ 黄子翾 ┆ null ┆ 13.5       ┆ 17.651723 │\n",
       "│ 4        ┆ 21.0     ┆ 空白对照组 ┆ 陈文翰 ┆ null ┆ 13.5       ┆ 17.651723 │\n",
       "│ 6        ┆ 0.0      ┆ 空白对照组 ┆ 陈文翰 ┆ null ┆ 13.5       ┆ 17.651723 │\n",
       "│ 8        ┆ 204.5    ┆ 利血平     ┆ 于小淞 ┆ null ┆ 146.583333 ┆ 65.183342 │\n",
       "│ 9        ┆ 178.0    ┆ 利血平     ┆ 王孟德 ┆ null ┆ 146.583333 ┆ 65.183342 │\n",
       "│ 10       ┆ 163.0    ┆ 利血平     ┆ 王孟德 ┆ null ┆ 146.583333 ┆ 65.183342 │\n",
       "│ 11       ┆ 180.0    ┆ 利血平     ┆ 王孟德 ┆ null ┆ 146.583333 ┆ 65.183342 │\n",
       "│ 12       ┆ 148.0    ┆ 利血平     ┆ 王孟德 ┆ null ┆ 146.583333 ┆ 65.183342 │\n",
       "└──────────┴──────────┴────────────┴────────┴──────┴────────────┴───────────┘"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import levene\n",
    "\n",
    "lf1_12 = lf1.filter(pl.col(\"所属组别\").is_in([\"空白对照组\", \"利血平\"]))\n",
    "\n",
    "lf1_12.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先进行方差齐性检验。提出假设：\n",
    "\n",
    "- $ H_0 $ : $ \\sigma_1{^2} = \\sigma_2{^2} $ ，即空白对照组和利血平模型对照组小鼠在悬尾实验中不动时间的总体方差相等。\n",
    "- $ H_1 $ : $ \\sigma_1{^2} \\neq \\sigma_2{^2} $ ，即空白对照组和利血平模型对照组小鼠在悬尾实验中不动时间的总体方差不相等。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "计算结果：LeveneResult(statistic=array([1.24350181]), pvalue=array([0.29717212]))\n",
      "方差齐性：True\n",
      "test statistic = 1.2435\n",
      "p-value = 0.2972\n",
      "alpha = 0.10\n",
      "Significance: False\n"
     ]
    }
   ],
   "source": [
    "x1 = lf1_12.filter(pl.col(\"所属组别\") == \"空白对照组\").select(\"不动时间\")\n",
    "x2 = lf1_12.filter(pl.col(\"所属组别\") == \"利血平\").select(\"不动时间\")\n",
    "\n",
    "result = levene(x1.drop_nulls().collect(), x2.drop_nulls().collect())\n",
    "\n",
    "print(f\"计算结果：{result}\")\n",
    "\n",
    "print(f\"方差齐性：{bool(result.pvalue[0] > 0.05)}\")\n",
    "\n",
    "print(\n",
    "    f\"\"\"test statistic = {result.statistic[0]:.4f}\n",
    "p-value = {result.pvalue[0]:.4f}\n",
    "alpha = 0.10\n",
    "Significance: {bool(result.pvalue[0] < 0.10)}\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$ P > 0.10 $ ，接受 $ H_0 $ ，认为空白对照组和利血平模型对照组的小鼠在悬尾事件中的不动时间的总体方差是相同的。因而可以用 t 检验比较两样本的总体均值是否相同。\n",
    "\n",
    "假设\n",
    "\n",
    "- $ H_0 $ : $ \\mu_1 = \\mu_2 $ ，即空白对照组和利血平模型对照组的小鼠在悬尾实验中的不动时间是相同的。\n",
    "- $ H_1 $ : $ \\mu_1 \\neq \\mu_2 $ ，即空白对照组和利血平模型对照组的小鼠在悬尾实验中的不动时间是不同的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-statistic = -16.2930\n",
      "p-value = 0.0000\n",
      "alpha = 0.05\n",
      "Significance: True\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "\n",
    "result = ttest_ind(x1.collect(), x2.collect(), equal_var=True, nan_policy=\"omit\")\n",
    "\n",
    "print(\n",
    "    f\"\"\"t-statistic = {result.statistic[0]:.4f}\n",
    "p-value = {result.pvalue[0]:.4f}\n",
    "alpha = 0.05\n",
    "Significance: {result.pvalue[0] < 0.05}\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$ p < 0.05 $ ，接受 $ H_0 $ ，可以认为利血平对小鼠在悬尾实验中的不动时间有显著的影响，造模是成功的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 强迫游泳实验\n",
    "\n",
    "空白对照组和模型对照组的实验数据如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (11, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>小鼠编号</th><th>不动时间</th><th>所属组别</th><th>记录人</th><th>备注</th><th>mean</th><th>sd</th></tr><tr><td>i64</td><td>i64</td><td>str</td><td>str</td><td>str</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>1</td><td>78</td><td>&quot;空白对照组&quot;</td><td>&quot;黄子翾&quot;</td><td>null</td><td>78.333333</td><td>23.499409</td></tr><tr><td>2</td><td>98</td><td>&quot;空白对照组&quot;</td><td>&quot;黄雨欣&quot;</td><td>null</td><td>78.333333</td><td>23.499409</td></tr><tr><td>3</td><td>104</td><td>&quot;空白对照组&quot;</td><td>&quot;黄雨欣&quot;</td><td>null</td><td>78.333333</td><td>23.499409</td></tr><tr><td>5</td><td>83</td><td>&quot;空白对照组&quot;</td><td>&quot;黄雨欣&quot;</td><td>null</td><td>78.333333</td><td>23.499409</td></tr><tr><td>6</td><td>76</td><td>&quot;空白对照组&quot;</td><td>&quot;黄雨欣&quot;</td><td>null</td><td>78.333333</td><td>23.499409</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>8</td><td>87</td><td>&quot;利血平&quot;</td><td>&quot;黄雨欣&quot;</td><td>null</td><td>119.333333</td><td>37.057013</td></tr><tr><td>9</td><td>78</td><td>&quot;利血平&quot;</td><td>&quot;黄雨欣&quot;</td><td>null</td><td>119.333333</td><td>37.057013</td></tr><tr><td>10</td><td>117</td><td>&quot;利血平&quot;</td><td>&quot;黄雨欣&quot;</td><td>null</td><td>119.333333</td><td>37.057013</td></tr><tr><td>11</td><td>156</td><td>&quot;利血平&quot;</td><td>&quot;黄子翾&quot;</td><td>null</td><td>119.333333</td><td>37.057013</td></tr><tr><td>12</td><td>98</td><td>&quot;利血平&quot;</td><td>&quot;黄子翾&quot;</td><td>null</td><td>119.333333</td><td>37.057013</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (11, 7)\n",
       "┌──────────┬──────────┬────────────┬────────┬──────┬────────────┬───────────┐\n",
       "│ 小鼠编号 ┆ 不动时间 ┆ 所属组别   ┆ 记录人 ┆ 备注 ┆ mean       ┆ sd        │\n",
       "│ ---      ┆ ---      ┆ ---        ┆ ---    ┆ ---  ┆ ---        ┆ ---       │\n",
       "│ i64      ┆ i64      ┆ str        ┆ str    ┆ str  ┆ f64        ┆ f64       │\n",
       "╞══════════╪══════════╪════════════╪════════╪══════╪════════════╪═══════════╡\n",
       "│ 1        ┆ 78       ┆ 空白对照组 ┆ 黄子翾 ┆ null ┆ 78.333333  ┆ 23.499409 │\n",
       "│ 2        ┆ 98       ┆ 空白对照组 ┆ 黄雨欣 ┆ null ┆ 78.333333  ┆ 23.499409 │\n",
       "│ 3        ┆ 104      ┆ 空白对照组 ┆ 黄雨欣 ┆ null ┆ 78.333333  ┆ 23.499409 │\n",
       "│ 5        ┆ 83       ┆ 空白对照组 ┆ 黄雨欣 ┆ null ┆ 78.333333  ┆ 23.499409 │\n",
       "│ 6        ┆ 76       ┆ 空白对照组 ┆ 黄雨欣 ┆ null ┆ 78.333333  ┆ 23.499409 │\n",
       "│ …        ┆ …        ┆ …          ┆ …      ┆ …    ┆ …          ┆ …         │\n",
       "│ 8        ┆ 87       ┆ 利血平     ┆ 黄雨欣 ┆ null ┆ 119.333333 ┆ 37.057013 │\n",
       "│ 9        ┆ 78       ┆ 利血平     ┆ 黄雨欣 ┆ null ┆ 119.333333 ┆ 37.057013 │\n",
       "│ 10       ┆ 117      ┆ 利血平     ┆ 黄雨欣 ┆ null ┆ 119.333333 ┆ 37.057013 │\n",
       "│ 11       ┆ 156      ┆ 利血平     ┆ 黄子翾 ┆ null ┆ 119.333333 ┆ 37.057013 │\n",
       "│ 12       ┆ 98       ┆ 利血平     ┆ 黄子翾 ┆ null ┆ 119.333333 ┆ 37.057013 │\n",
       "└──────────┴──────────┴────────────┴────────┴──────┴────────────┴───────────┘"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lf2_12 = lf2.filter(pl.col(\"所属组别\").is_in([\"空白对照组\", \"利血平\"]))\n",
    "\n",
    "lf2_12.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "进行方差齐性检验。提出假设：\n",
    "\n",
    "- $ H_0 $ : $ \\sigma_1{^2} = \\sigma_2{^2} $ ，即空白对照组和利血平模型对照组小鼠在强迫游泳实验中不动时间的总体方差相等。\n",
    "- $ H_1 $ : $ \\sigma_1{^2} \\neq \\sigma_2{^2} $ ，即空白对照组和利血平模型对照组小鼠在强迫游泳实验中不动时间的总体方差不相等。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "方差齐性：True\n",
      "test statistic = 3.5763\n",
      "p-value = 0.0912\n",
      "alpha = 0.10\n",
      "Significance: True\n"
     ]
    }
   ],
   "source": [
    "x2_1 = lf2_12.filter(pl.col(\"所属组别\") == \"空白对照组\").select(\"不动时间\")\n",
    "x2_2 = lf2_12.filter(pl.col(\"所属组别\") == \"利血平\").select(\"不动时间\")\n",
    "\n",
    "result = levene(x2_1.collect(), x2_2.collect(), nan_policy=\"omit\")\n",
    "\n",
    "print(f\"方差齐性：{bool(result.pvalue[0] > 0.05)}\")\n",
    "\n",
    "print(\n",
    "    f\"\"\"test statistic = {result.statistic[0]:.4f}\n",
    "p-value = {result.pvalue[0]:.4f}\n",
    "alpha = 0.10\n",
    "Significance: {bool(result.pvalue[0] < 0.1)}\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$ p < 0.10 $ ，拒绝 $ H_0 $ ，认为空白对照组和利血平模型对照组小鼠在强迫游泳实验中不动时间的总体方差不相等。采用 Welch 法 t' 检验判断两样本的总体均值是否相同。\n",
    "\n",
    "假设\n",
    "\n",
    "- $ H_0 $ : $ \\mu_1 = \\mu_2 $ ，即空白对照组和利血平模型对照组的小鼠在强迫游泳实验中的不动时间是相同的。\n",
    "- $ H_1 $ : $ \\mu_1 \\neq \\mu_2 $ ，即利血平模型对照组的小鼠相较空白对照组和在强迫游泳实验中的不动时间更长。。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t statistic = -1.6593\n",
      "p-value = 0.1314\n",
      "alpha = 0.05\n",
      "Significance: False\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "\n",
    "res = ttest_ind(x2_1.collect(), x2_2.collect(), equal_var=True, alternative=\"two-sided\")\n",
    "\n",
    "print(\n",
    "    f\"\"\"t statistic = {res.statistic[0]:.4f}\n",
    "p-value = {res.pvalue[0]:.4f}\n",
    "alpha = 0.05\n",
    "Significance: {bool(res.pvalue[0] < 0.05)}\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$ p > 0.05 $ ，接受 $ H_0 $ ，认为空白对照组和利血平模型对照组的小鼠在强迫游泳实验中的不动时间相同，即利血平造模不能导致小鼠在强迫游泳实验中的不动时间显著增加。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 实验组显著性分析\n",
    "\n",
    "分析实验组的不同声音刺激相较于利血平模型对照组在动物行为学测试的指标上有无显著性差异。\n",
    "\n",
    "#### 悬尾实验\n",
    "\n",
    "结果变量为多项无序分类变量，影响变量为多项无序分类变量，可以采用完全随机设计的方差分析 (ANOVA) 推断各个组别的差异水平。\n",
    "\n",
    "方差分析需要在样本方差相同的前提下进行。首先进行方差齐性检验。\n",
    "\n",
    "##### 方差齐性检验\n",
    "\n",
    "提出假设\n",
    "\n",
    "- $ H_0 $ : 模型对照组及各个声音刺激实验组的小鼠在悬尾实验中的行为学测试指标的总体方差无显著差异。  \n",
    "- $ H_1 $ : 模型对照组及各个声音刺激实验组的小鼠在悬尾实验中的行为学测试指标的总体方差有显著差异。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test statistic = 0.9962\n",
      "p-value = 0.4184\n",
      "alpha = 0.10\n",
      "Significance: False\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import levene\n",
    "\n",
    "x1_2 = lf1.filter(pl.col(\"所属组别\")=='利血平').select(\"不动时间\")\n",
    "x1_3 = lf1.filter(pl.col(\"所属组别\")=='利血平+大三度').select(\"不动时间\")\n",
    "x1_5 = lf1.filter(pl.col(\"所属组别\")=='利血平+纯五度').select(\"不动时间\")\n",
    "x1_9 = lf1.filter(pl.col(\"所属组别\")=='利血平+小九度').select(\"不动时间\")\n",
    "\n",
    "\n",
    "res = levene(x1_2.collect(), x1_3.collect(), x1_5.collect(), x1_9.collect())\n",
    "\n",
    "print(\n",
    "    f\"\"\"test statistic = {res.statistic[0]:.4f}\n",
    "p-value = {res.pvalue[0]:.4f}\n",
    "alpha = 0.10\n",
    "Significance: {res.pvalue[0] < 0.10}\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$ p > 0.05 $ , 接受 $ H_0 $ ，认为模型对照组及各个声音刺激实验组的小鼠在悬尾实验中的行为学测试指标的总体方差没有显著差异。可以使用 One-way ANOVA 分析样本的均值是否相同。\n",
    "\n",
    "##### One-way ANOVA\n",
    "\n",
    "提出假设\n",
    "\n",
    "- $ H_0 $ : 模型对照组及各个声音刺激实验组的小鼠在悬尾实验中的行为学测试指标的总体均值无显著差异。  \n",
    "- $ H_1 $ : 模型对照组及各个声音刺激实验组的小鼠在悬尾实验中的行为学测试指标的总体均值有显著差异。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-statistic = 6.1506\n",
      "p-value = 0.0050\n",
      "Significant: True\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import f_oneway\n",
    "\n",
    "res = f_oneway(x1_2.collect(), x1_3.collect(), x1_5.collect(), x1_9.collect())\n",
    "\n",
    "print(\n",
    "    f\"\"\"F-statistic = {res.statistic[0]:.4f}\n",
    "p-value = {res.pvalue[0]:.4f}\n",
    "Significant: {res.pvalue[0] < 0.05}\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$ p < 0.05 $ ，拒绝 $ H_0 $ , 认为模型对照组及各个声音刺激实验组的小鼠在悬尾实验中的行为学测试指标的总体均值有显著差异，即不同音程的声音刺激可以影响小鼠的抑郁程度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1920x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# means = lf1.filter(pl.col(\"所属组别\").is_in(['利血平','利血平+大三度','利血平+纯五度','利血平+小九度']))\n",
    "def _get_mean(lf: pl.LazyFrame) -> float:\n",
    "    return lf.collect().to_series().mean()\n",
    "def _get_se(lf: pl.LazyFrame) -> float:\n",
    "    return lf.collect().to_series().std() / (lf.collect().shape[0] ** 0.5)\n",
    "\n",
    "x1_1 = lf1.filter(pl.col(\"所属组别\") == '空白对照组').select(pl.col(\"不动时间\"))\n",
    "\n",
    "means = np.array(list(map(_get_mean, [x1_1,x1_2,x1_3,x1_5,x1_9])))\n",
    "ses = np.array(list(map(_get_se, [x1_1,x1_2,x1_3,x1_5,x1_9])))\n",
    "\n",
    "sns.set_style('whitegrid', rc=rc)\n",
    "\n",
    "fig, ax = plt.subplots(dpi=300)\n",
    "\n",
    "# strip plot\n",
    "\n",
    "sns.stripplot(\n",
    "    data=lf1.filter(pl.col(\"所属组别\").is_in(['空白对照组','利血平','利血平+大三度','利血平+纯五度','利血平+小九度'])).collect(),\n",
    "    x=\"所属组别\",\n",
    "    y=\"不动时间\",\n",
    "    hue=\"所属组别\",\n",
    "    jitter=True,\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "# errorbar\n",
    "ax.errorbar(\n",
    "    x=['空白对照组','利血平','利血平+大三度','利血平+纯五度','利血平+小九度'],\n",
    "    y=means,\n",
    "    yerr=ses,\n",
    "    fmt='.',\n",
    "    label=r\"3 $ \\times $ standard error\",\n",
    "    capsize=5,\n",
    "    color='grey'\n",
    ")\n",
    "\n",
    "ax.spines[\"bottom\"].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "fig.legend()\n",
    "fig.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可见各实验组相较于模型对照组，小鼠的不动时间都有所增加，而其中“利血平+纯五度音程刺激”组中小鼠不动时间的增加尤为显著。这也许说明了，播放纯五度音程音频，对小鼠的致抑郁作用最为显著。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 强迫游泳实验\n",
    "\n",
    "同样地，结果变量为多项无序分类变量，影响变量为多项无序分类变量，可以采用完全随机设计的方差分析 (ANOVA) 推断各个组别的差异水平。在进行方差分析之前，要确保数据的方差齐性。\n",
    "\n",
    "##### 方差齐性检验\n",
    "\n",
    "提出假设\n",
    "\n",
    "- $ H_0 $ : 模型对照组及各个声音刺激实验组的小鼠在强迫游泳实验中的行为学测试指标的总体方差无显著差异。  \n",
    "- $ H_1 $ : 模型对照组及各个声音刺激实验组的小鼠在强迫游泳实验中的行为学测试指标的总体方差有显著差异。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test statistic = 1.1467\n",
      "p-value = 0.3558\n",
      "alpha = 0.10\n",
      "Significant: False\n"
     ]
    }
   ],
   "source": [
    "x2_2 = lf2.filter(pl.col(\"所属组别\")=='利血平').select(\"不动时间\")\n",
    "x2_3 = lf2.filter(pl.col(\"所属组别\")=='利血平+大三度').select(\"不动时间\")\n",
    "x2_5 = lf2.filter(pl.col(\"所属组别\")=='利血平+纯五度').select(\"不动时间\")\n",
    "x2_9 = lf2.filter(pl.col(\"所属组别\")=='利血平+小九度').select(\"不动时间\")\n",
    "\n",
    "res = levene(x2_2.collect(), x2_3.collect(), x2_5.collect(), x2_9.collect())\n",
    "\n",
    "print(\n",
    "    f\"\"\"test statistic = {res.statistic[0]:.4f}\n",
    "p-value = {res.pvalue[0]:.4f}\n",
    "alpha = 0.10\n",
    "Significant: {res.pvalue[0] < 0.10}\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$ p > 0.10 $ ，接受 $ H_0 $ ，认为模型对照组及各个声音刺激实验组的小鼠在强迫游泳实验中的行为学测试指标的总体方差无显著差异。可以使用 One-way ANOVA 推断各组的总体均值是否相等。\n",
    "\n",
    "##### One-way ANOVA\n",
    "\n",
    "提出假设\n",
    "\n",
    "- $ H_0 $ : 模型对照组及各个声音刺激实验组的小鼠在强迫游泳实验中的行为学测试指标的总体均值无显著差异。  \n",
    "- $ H_1 $ : 模型对照组及各个声音刺激实验组的小鼠在强迫游泳实验中的行为学测试指标的总体均值有显著差异。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-statistic = 1.1707\n",
      "p-value = 0.3470\n",
      "alpha = 0.05\n",
      "Significant: False\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import f_oneway\n",
    "\n",
    "res = f_oneway(x2_2.collect(), x2_3.collect(), x2_5.collect(), x2_9.collect())\n",
    "\n",
    "print(\n",
    "    f\"\"\"F-statistic = {res.statistic[0]:.4f}\n",
    "p-value = {res.pvalue[0]:.4f}\n",
    "alpha = 0.05\n",
    "Significant: {res.pvalue[0] < 0.05}\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$ p > 0.05 $ ，接受 $ H_0 $ ，认为模型对照组及各个声音刺激实验组的小鼠在强迫游泳实验中的行为学测试指标的总体均值无显著差异。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1920x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# means = lf1.filter(pl.col(\"所属组别\").is_in(['利血平','利血平+大三度','利血平+纯五度','利血平+小九度']))\n",
    "def _get_mean(lf: pl.LazyFrame) -> float:\n",
    "    return lf.collect().to_series().mean()\n",
    "def _get_se(lf: pl.LazyFrame) -> float:\n",
    "    return lf.collect().to_series().std() / (lf.collect().shape[0] ** 0.5)\n",
    "\n",
    "x2_1 = lf2.filter(pl.col(\"所属组别\") == '空白对照组').select(pl.col(\"不动时间\"))\n",
    "\n",
    "means = np.array(list(map(_get_mean, [x2_1,x2_2,x2_3,x2_5,x2_9])))\n",
    "ses = np.array(list(map(_get_se, [x2_1,x2_2,x2_3,x2_5,x2_9])))\n",
    "\n",
    "sns.set_style('whitegrid', rc=rc)\n",
    "\n",
    "fig, ax = plt.subplots(dpi=300)\n",
    "\n",
    "# strip plot\n",
    "\n",
    "sns.stripplot(\n",
    "    data=lf2.filter(pl.col(\"所属组别\").is_in(['空白对照组','利血平','利血平+大三度','利血平+纯五度','利血平+小九度'])).collect(),\n",
    "    x=\"所属组别\",\n",
    "    y=\"不动时间\",\n",
    "    hue=\"所属组别\",\n",
    "    jitter=True,\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "# errorbar\n",
    "ax.errorbar(\n",
    "    x=['空白对照组','利血平','利血平+大三度','利血平+纯五度','利血平+小九度'],\n",
    "    y=means,\n",
    "    yerr=ses,\n",
    "    fmt='.',\n",
    "    label=r\"3 $ \\times $ standard error\",\n",
    "    capsize=5,\n",
    "    color='grey'\n",
    ")\n",
    "\n",
    "ax.spines[\"bottom\"].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "fig.legend()\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
